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In recent years the introduction and subsequent enhancements of information technology has seen the emergence of major opportunities for developing new approaches to improve information interchange. The provision of information provides opportunities to find resolutions to problem situations, and the power of providing relevant and accurate information through using information systems cannot be underestimated. However, the world of information systems thinking has, so far, experienced difficulty finding a methodology suitable for utilizing the full range of available current information technology. This situation in the real world shows itself predominantly as information starvation as current information systems struggle to bring order and structure to information technology developments. This paper will discuss the issues relating to the development of a new generic framework. The novel aspect of this generic framework for planning information systems is that it integrates Soft Systems Methodology (SSM) and Viable Systems Model (VSM) in a coherent whole, whilst embracing the concepts of self-adaptation and autonomy. Further, the generic framework integrates the spatial analysis capabilities of a Geographical Information System (GIS). The generic framework will then be tested in real-world problem situations in order to test its effectiveness, efficiency and efficacy with regards to finding suitable solutions to the problem of information starvation. The importance of focusing the framework on GIS functionality is related to the fact that in many organizations GIS is employed as the solution to information starvation because of its ability to display spatial information. This paper will demonstrate the implementation of the generic framework in a UK Police Authority, a large emergency service that suffers from information starvation. In the context of a UK Police Authority the combination of SSM and VSM being supported by GIS functionality is seen as being a significant progression in relation to crime detection and operational planning.

In this paper, we examine the management of unintentional dwelling fire risk through the development of a geographical information system (GIS) for dwelling fire prevention support based upon an 18-month case study in a UK fire and rescue service. Previous research into causal factors in unintentional dwelling fire incidents was used to guide the development of a multiple linear regression risk model for dwelling fire incidents that was the basis of the GIS developed. The GIS provided a more detailed analysis of unintentional dwelling fire risk factors, and enabled more targeted fire prevention activities for the identified atrisk social groups. IntroductionIn this paper, we examine the management of unintentional dwelling fire risk through the development of a geographical information system (GIS) for modelling the risk of dwelling fires within the area covered by a UK fire and rescue service. The purpose of the GIS was to assist in the reduction of unintentional dwelling fires and fatalities within a given geographical area in the UK via more targeted fire prevention initiatives. Fire prevention initiatives (Brussoni, Towner, and Hayes 2006;Rosenberg 1999;Shai 2006;Smith et al. 2007) are increasingly being viewed as an effective means of reducing incidents of unintentional dwelling fires, fire injuries and fire fatalities. In addition, from a resources viewpoint, fire prevention initiatives are increasingly considered to be an effective and efficient utilisation of fire and rescue service personnel.The fire and rescue service studied had previously utilised internal data relating to accidental dwelling fires along with indices of multiple deprivation in the area covered to determine patterns of unintentional dwelling fire incidence. This was used to inform and direct fire prevention activities. However, in order to attempt the management of unintentional dwelling fire risk through improved effectiveness of fire prevention activities, the fire and rescue service studied wished to be able to more specifically target at-risk groups for fire prevention initiatives. Initially, statistical analyses of the fire and rescue service internal accidental dwelling fire data were conducted to determine if such could provide information concerning at-risk groups. However, no significant patterns emerged from the data. The decision was then taken that data from outside organisations would need to be utilised in order to